DeePKS-kit is a pure python library so it can be installed following the standard `git clone` then `pip install` procedure. Note that the two main requirements `pytorch` and `pyscf` will not be installed automatically so you will need to install them manually in advance. Below is a more detailed instruction that includes installing the required libraries in the environment.
We use `conda` here as an example. So first you may need to install [Anaconda](https://docs.anaconda.com/anaconda/install/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
To reduce the possibility of library conflicts, we suggest create a new environment (named `deepks`) with basic dependencies installed (optional):
An relatively detailed decrisption of the `deepks-kit` library can be found in [here](https://arxiv.org/pdf/2012.14615.pdf). Please also refer to the reference for the description of methods.
Please see [`examples`](./examples) folder for the usage of `deepks-kit` library. A detailed example with executable data for single water molecules can be found [here](./examples/water_single). A more complicated one for training water clusters can be found [here](./examples/water_cluster).
| || DCU | GPU |
| ------ | ------| ------ |
|总训练用时/s|| 23.87 | 27.36 |
|每100epoch用时/s|| 2.38 | 2.73 |
|每100epoch测试/s|| 0.44 | 2.43 |
Check [this input file](./examples/water_cluster/args.yaml) for detailed explanation for possible input parameters, and also [this one](./examples/water_cluster/shell.yaml) if you would like to run on local machine instead of using Slurm scheduler.
## References
## 源码仓库及问题反馈
[1] Chen, Y., Zhang, L., Wang, H. and E, W., 2020. Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy. The Journal of Physical Chemistry A, 124(35), pp.7155-7165.
https://github.com/deepmodeling/deepks-kit
[2] Chen, Y., Zhang, L., Wang, H. and E, W., 2021. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. Journal of Chemical Theory and Computation, 17(1), pp.170–181.
## 参考
[1] Chen, Y., Zhang, L., Wang, H. and E, W.,2023. DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models, Computer Physics Communications, 282, 108520. https://doi.org/10.1016/j.cpc.2022.108520.
